Dynamically adjustable reference line sampling point density for autonomous vehicles

ABSTRACT

According to some embodiments, a system receives a first set of reference points based on a map and a route information, the first plurality of reference points representing a reference line in which the ADV is to follow. The system selects a second set of reference points along the reference line, including iteratively performing, selecting a current reference point from the first set of reference points, determining a sampling distance along the first set of reference points based on the currently selected reference point using a nonlinear algorithm, and selecting a next reference point based on the determined sampling distance such that a density of the second set of reference points closer to the ADV is higher than a density of the selected reference points farther away from the ADV. The system plans a trajectory for the ADV using the second set of reference points to control the ADV.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to dynamically adjusted reference line sampling point density forautonomous driving vehicles (ADVs).

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Vehicles can operate in an autonomous mode by a planned drivingtrajectory. A planned driving trajectory is generated by an ADV'splanning module relying on “reference lines”. A reference line is asmooth line on a geometric word map. Reference lines are represented bya set of points along a road curve. A typical 200 meter road curve uses1000 points for a reference line. The computation time for manyalgorithms, such as reference line projections and driving trajectorydecision and planning, are directly related to the number of points ordensity of a reference line.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of a sensor andcontrol system using by an autonomous vehicle according to oneembodiment.

FIGS. 3A-3B are block diagrams illustrating examples of a perception andplanning system used by an autonomous vehicle according to someembodiments.

FIG. 4 is a block diagram illustrating an example reference line pointswith equidistance.

FIG. 5 is a block diagram illustrating an example dynamically adjustedreference line sampling point density according to one embodiment.

FIG. 6 is a flow diagram illustrating a method according to oneembodiment.

FIG. 7 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, a system dynamically adjusts a referenceline sampling point density. In one embodiment, the system receives afirst set of reference points based on a map and a route information,the first plurality of reference points representing a reference line inwhich the ADV is to follow. The system selects a second set of referencepoints along the reference line, including iteratively performing,selecting a current reference point from the first set of referencepoints, determining a sampling distance along the first set of referencepoints based on the currently selected reference point using a nonlinearalgorithm, and selecting a next reference point based on the determinedsampling distance such that a density of the second set of referencepoints closer to the ADV is higher than a density of the selectedreference points farther away from the ADV. The system plans atrajectory for the ADV using the second set of reference points tocontrol the ADV.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyword, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or models 124 for a variety ofpurposes. In one embodiment, for example, algorithms/model 124 mayinclude a sampling model or algorithm that can dynamically adjust areference line sampling point density. The sampling model can includeminimum sampling distances, maximum sampling distances, and one or moresampling algorithms to modify a reference line sampling point density.The sampling model can be uploaded onto the autonomous driving vehicleto be used to dynamically adjust a reference line point density of areference line at real time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing/sampling module 307, and reference line generator309.

Some or all of modules 301-309 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-309may be integrated together as an integrated module. For example,routing/sampling module 307 and reference line generator 309 may beintegrated as a single module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration (e.g., straight or curvelanes), traffic light signals, a relative position of another vehicle, apedestrian, a building, crosswalk, or other traffic related signs (e.g.,stop signs, yield signs), etc., for example, in a form of an object.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts how the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle). That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, and turning commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as command cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or command cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to effect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Decision module 304/planning module 305 may further include a collisionavoidance system or functionalities of a collision avoidance system toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle. For example, the collisionavoidance system may effect changes in the navigation of the autonomousvehicle by operating one or more subsystems in control system 111 toundertake swerving maneuvers, turning maneuvers, braking maneuvers, etc.The collision avoidance system may automatically determine feasibleobstacle avoidance maneuvers on the basis of surrounding trafficpatterns, road conditions, etc. The collision avoidance system may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, etc. in theregion adjacent the autonomous vehicle that would be swerved into. Thecollision avoidance system may automatically select the maneuver that isboth available and maximizes safety of occupants of the autonomousvehicle. The collision avoidance system may select an avoidance maneuverpredicted to cause the least amount of acceleration in a passenger cabinof the autonomous vehicle.

Routing module 307 can generate reference lines, for example, from mapinformation such as information of road segments, vehicular lanes ofroad segments, and distances from lanes to curb. For example, a road canbe divided into sections or segments {A, B, and C} to denote three roadsegments. Three lanes of road segment A can be enumerated {A1, A2, andA3}. A reference line is generated by generating reference points ofuniform density along the reference line. For example, for a vehicularlane, routing module 307 can connect midpoints of two opposing curbs orextremities of the vehicular lane provided by a map data. Based on themidpoints and machine learning data representing collected data pointsof vehicles previously driven on the vehicular lane at different pointsin time, routing module 307 can calculate the reference points byselecting a subset of the collected data points within a predeterminedproximity of the vehicular lane and applying a smoothing function to themidpoints in view of the subset of collected data points.

Based on reference points, reference line generator 309 may generate areference line by interpolating the reference points such that thegenerated reference line is used as a reference line for controllingADVs on the vehicular lane. In some embodiments, a reference pointstable and a road segments table representing the reference lines aredownloaded in real-time to ADVs such that the ADVs can generatereference lines from the reference points table and the road segmentstable based on the ADVs' geographical location and driving direction.For example, in one embodiment, an ADV can generate a reference line byrequesting routing service for a path segment by a path segmentidentifier representing an upcoming road section ahead and/or based onthe ADV's GPS location. Based on a path segment identifier, a routingservice can return to the ADV reference points table containingreference points for all lanes of road segments of interest. ADV canlook up reference points for a lane for a path segment to generate areference line for controlling the ADV on the vehicular lane.

In one embodiment, to reduce a computational load of the ADV, samplingmodule 307 can determine a minimum sampling distance and a maximumsampling distance for a length of road to dynamically adjust a densityof the generated reference line for a reference line with a non-uniformline density. For example, sampling module 307 can apply a samplingalgorithm (as part of sampling algorithm/model 313) to dynamicallyadjust a density of the generated reference line to reduce the number ofreference points of the reference line based on availability ofcomputational resources or to satisfy some objective requirements. Forexample, sampling module 307 may receive a request from perception andplanning system 110 (by way of settings change via a user interface, oran update downloaded to the ADV via server 104) to reduce a referencepoint density by about five fold without a change to the reference linepoint density near the ADV.

Sampling module 307 may dynamically adjust the reference line pointdensity by adjusting a maximum sampling distance and calculating a newreference line point density by trial and error until the samplingmodule 307 reaches the computational objective requirements. In onescenario, sampling module 307 performs a sampling algorithm calculationhaving inputs of minimum sampling distance, maximum sampling distance,and sampling length. Sampling module 307 may try a maximum samplingdistance of 0.9, 1.0, 1.1, 1.2, etc. meters, while keeping constant aminimum sampling distance of 0.2 meters (e.g., ensuring a reference linepoint density near the ADV is about 0.2 meters/point) and a road lengthof 200 meters, to acquire a new reference line with point density havinga five-fold reduction. The newly acquired maximum sampling distance thencan be used to generate subsequent non-uniform reference line points,accordingly.

As described above, route or routing module 307 manages any data relatedto a trip or route of a user. The user of the ADV specifies a startingand a destination location to obtain trip related data. Trip relateddata includes route segments and a reference line or reference points ofthe route segment. For example, based on route map info 311, routemodule 307 generates a route or road segments table and a referencepoints table. The reference points are in relations to road segmentsand/or lanes in the road segments table. The reference points can beinterpolated to form one or more reference lines to control the ADV. Thereference points can be specific to road segments and/or specific lanesof road segments.

For example, a road segments table can be a name-value pair to includeprevious and next road lanes for road segments A-D. E.g., a roadsegments table may be: {(A1, B1), (B1, C1), (C1, D1)} for road segmentsA-D having lane 1. A reference points table may include reference pointsin x-y coordinates for road segments lanes, e.g., {(A1, (x1, y1)), (B1,(x2, y2)), (C1, (x3, y3)), (D1, (x4, y4))}, where A1 . . . D1 refers tolane 1 of road segments A-D, and (x1, y1) . . . (x4, y4) arecorresponding real world coordinates. In one embodiment, road segmentsand/or lanes are divided into a predetermined length such asapproximately 200 meters segments/lanes. In another embodiment, roadsegments and/or lanes are divided into variable length segments/lanesdepending on road conditions such as road curvatures. In someembodiments, each road segment and/or lane can include several referencepoints. In some embodiments, reference points can be converted to othercoordinate systems, e.g., latitude-longitude. Sampling module 307 canthen dynamically adjust a density of the reference points.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 4 is a block diagram illustrating an example reference line pointsbefore a sampling algorithm/model is applied. Referring to FIG. 4,reference line 401 represents a set of reference points 402 startingfrom ADV 101. Reference points 402 may be provided by routing module 407based on a current location of the ADV and a destination location to bereached. Reference points 402 (e.g., a first set of reference points)may be in an (x, y) real world coordinate system or alongitudinal-latitudinal coordinate system. In one example, referencepoints 402 (from initial point 403 to end point 404) can have pointsapproximately 0.2 meters apart representing reference line 401 of length200 meters.

FIG. 5 is a block diagram illustrating an example dynamically adjustedreference line sampling point density according to one embodiment. Inone embodiment, referring to FIG. 5, reference line 401 includesreference points 403, 502-506 (e.g., a second set of reference points)with a non-uniform density. Reference points 502-506 can be selectedalong reference line 401 by sampling module 307 using a samplingalgorithm/model (as part of sampling algorithm/model 313). A samplealgorithm can calculate a non-uniform density of reference pointprovided: a minimum sample point distance, a maximum sample pointdistance, and a length of the reference line. In one embodiment, thesampling algorithm is:

${{f(s)} = \frac{A}{1 + e^{{Bs} + C}}};{A = {Dmax}};{C = {\log\left( \frac{A}{{Dmin} - 1} \right)}};$${B = {\left( {{\log\left( \frac{A}{\left( {A - \sigma} \right) - 1} \right)} - C} \right)\text{/}L}},$where s is a distance from the ADV of a current sampling reference pointalong the first set of reference points, Dmax is a maximum samplingpoint distance, Dmin is a minimum sampling point distance, L is a lengthof the reference line, and σ is a small number such as 1e-3.

Referring to FIG. 5, for the purpose of illustration, reference line 401can include reference points 403, 502-506 with a non-uniform density.Reference points 502-506 can be selected along reference line 401 or itscorresponding reference points by sampling module 307. In oneembodiment, sampling module 307 starts at initial point 403 (e.g., s=0)and apply a sampling algorithm at the initial point s=0 to calculate asampling point distance f(s) 514, at s=0. Sampling module 307 thenselects the next sampling point 503 which is sampling point distance 514away from point 403. Sampling module 307 applies the sampling algorithmat point 503 to calculate sampling point distance 515 to select point504. Sampling module 307 applies the sampling algorithm to subsequentsampling points (504 . . . 506) to calculate subsequent sampling pointdistances to select subsequent sampling points until the samplingalgorithm reaches an end point, such as point 506. This way a referenceline with a non-uniform density of reference points (e.g., points 403,502-506) can be utilized subsequently by other modules, such as decisionand planning modules 304-305, to plan a driving trajectory to controlADV 101. The motivation of controlling a density of the reference pointsis to be able to reduce a computational time of many algorithms, such asalgorithms in prediction, decision, and planning cycles, which arelinearly related to the number of points on a reference line. Areference line with fewer reference points can lead to more efficientand faster cycles. However, reducing the number of reference points orincreasing the sampling intervals between reference points (especiallycloser to the ADV) may introduce errors such as errors in not being ableto accurately capture events occurring between adjacent reference pointsthat could otherwise be captured. Thus, diminishing the density of thereference points as distance gets farther away from the ADV would notintroduce such errors near the immediate driving range of the ADV whileat the same time would allow a performance gain in computationalefficiency as distance is farther away from the ADV.

Since the reference points located farther away from the vehicle, it isunlikely those reference points will be utilized to control the ADV in acurrent cycle. When a next planning cycle comes, a new reference line isgenerated and the previous reference line of the previous planning cyclemay not be utilized. Thus, by sampling the reference points near thevehicle at a higher density level we can ensure the trajectory will begenerated accurately. By sampling the reference points farther away fromthe ADV at a lower density level, which are unlikely used to control theADV in the current planning cycle, we can reduce the computation time.

FIG. 6 is a flow diagram illustrating a method to control an ADVaccording to one embodiment. Processing 600 may be performed byprocessing logic which may include software, hardware, or a combinationthereof. For example, process 600 may be performed by sampling module307 of FIGS. 3A-3B. Referring to FIG. 6, at block 601, processing logicreceives a first set of reference points based on a map and a routeinformation, the first set of reference points representing a referenceline in which the ADV is to follow. At block 602, processing logicselects a second set of reference points along the reference linecorresponding to the first set of reference points, includingiteratively performing, at block 603, selecting a current referencepoint from the first set of reference points. At block 604, determininga sampling distance along the first set of reference points based on thecurrently selected reference point using a nonlinear algorithm. At block605, selecting a next reference point along the first set of referencepoints based on the determined sampling distance such that a density ofthe second set of reference points close to the ADV is higher than adensity of the selected reference points farther away from the ADV. Atblock 606, processing logic plans a trajectory for the ADV using thesecond set of reference points to control the ADV.

In one embodiment, determining a sampling distance further includesdetermining a minimum sampling distance and determining a maximumsampling distance, where the sampling distance is determined based onthe minimum sampling distance and the maximum sampling distance. Inanother embodiment, the sampling distance is determined further in viewof a length of the reference line.

In one embodiment, the second set of reference points includes pointsthat are separated by a distance approximately equal to the minimumsampling distances near the ADV and a distance approximately equal tothe maximum sampling distances away from the ADV. In one embodiment, thesecond set of reference points has a density that gradually decreasesaway from the ADV.

In one embodiment, the nonlinear algorithm is an inverse of anexponential function. In one embodiment, the nonlinear algorithm is

$\frac{A}{1 + e^{{Bx} + C}},$where A is a maximum sampling distance, C is a logarithmic of A dividedby a minimum sampling distance minus one, B is a logarithmic ofA/((A−σ)−1), minus C, divided by a length of the reference line, and σis a fractional number.

FIG. 7 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 orservers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS/iOS from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, routing/sampling module 307 of FIG. 3.Processing module/unit/logic 1528 may also reside, completely or atleast partially, within memory 1503 and/or within processor 1501 duringexecution thereof by data processing system 1500, memory 1503 andprocessor 1501 also constituting machine-accessible storage media.Processing module/unit/logic 1528 may further be transmitted or receivedover a network via network interface device 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method to generate adriving trajectory with progressive sampling distance for an autonomousdriving vehicle (ADV), the method comprising: receiving a first set ofreference points based on a map and a route information, the first setof reference points representing a reference line in which the ADV is tofollow; selecting a second set of reference points along the referenceline corresponding to the first set of reference points, includingiteratively performing, selecting a current reference point from thefirst set of reference points; determining a sampling distance along thefirst set of reference points based on the currently selected referencepoint using a nonlinear algorithm; and selecting a next reference pointalong the first set of reference points based on the determined samplingdistance such that a density of the selected reference points closer tothe ADV is higher than a density of the selected reference pointsfarther away from the ADV; and planning a trajectory for the ADV usingthe second set of reference points to control the ADV.
 2. Thecomputer-implemented method of claim 1, wherein determining a samplingdistance further comprises: determining a minimum sampling distance; anddetermining a maximum sampling distance, wherein the sampling distanceis determined based on the minimum sampling distance and the maximumsampling distance.
 3. The computer-implemented method of claim 2,wherein the sampling distance is determined further in view of a lengthof the reference line.
 4. The computer-implemented method of claim 1,wherein the second set of reference points includes points that areseparated by a distance approximately equal to the minimum samplingdistances near the ADV and a distance approximately equal to the maximumsampling distances away from the ADV.
 5. The computer-implemented methodof claim 1, wherein the second set of reference points has a densitythat gradually decreases away from the ADV.
 6. The computer-implementedmethod of claim 1, wherein the nonlinear algorithm is an inverse of anexponential function.
 7. The computer-implemented method of claim 1,wherein the nonlinear algorithm is${{f(s)} = \frac{A}{1 + e^{{Bs} + C}}},$ wherein s is a distance awayfrom the ADV, A is a maximum sampling distance, C is a logarithmic of Adivided by a minimum sampling distance minus one, and B is a logarithmicof A/((A−σ)−1), minus C, divided by a length of the reference line,wherein σ is a fractional number.
 8. A non-transitory machine-readablemedium having instructions stored therein, which when executed by aprocessor, cause the processor to perform operations, the operationscomprising: receiving a first set of reference points based on a map anda route information, the first set of reference points representing areference line in which the ADV is to follow; selecting a second set ofreference points along the reference line corresponding to the first setof reference points, including iteratively performing, selecting acurrent reference point from the first set of reference points;determining a sampling distance along the first set of reference pointsbased on the currently selected reference point using a nonlinearalgorithm; and selecting a next reference point along the first set ofreference points based on the determined sampling distance such that adensity of the selected reference points closer to the ADV is higherthan a density of the selected reference points farther away from theADV; and planning a trajectory for the ADV using the second set ofreference points to control the ADV.
 9. The non-transitorymachine-readable medium of claim 8, wherein determining a samplingdistance further comprises: determining a minimum sampling distance; anddetermining a maximum sampling distance, wherein the sampling distanceis determined based on the minimum sampling distance and the maximumsampling distance.
 10. The non-transitory machine-readable medium ofclaim 9, wherein the sampling distance is determined further in view ofa length of the reference line.
 11. The non-transitory machine-readablemedium of claim 8, wherein the second set of reference points includespoints that are separated by a distance approximately equal to theminimum sampling distances near the ADV and a distance approximatelyequal to the maximum sampling distances away from the ADV.
 12. Thenon-transitory machine-readable medium of claim 8, wherein the secondset of reference points has a density that gradually decreases away fromthe ADV.
 13. The non-transitory machine-readable medium of claim 8,wherein the nonlinear algorithm is an inverse of an exponentialfunction.
 14. The non-transitory machine-readable medium of claim 8,wherein the nonlinear algorithm is${{f(s)} = \frac{A}{1 + e^{{Bs} + C}}},$ wherein s is a distance awayfrom the ADV, A is a maximum sampling distance, C is a logarithmic of Adivided by a minimum sampling distance minus one, and B is a logarithmicof A/((A−σ)−1), minus C, divided by a length of the reference line,wherein σ is a fractional number.
 15. A data processing system,comprising: one or more processors; and a memory coupled to the one ormore processors to store instructions, which when executed by the one ormore processors, cause the processor to perform operations, theoperations including receiving a first set of reference points based ona map and a route information, the first set of reference pointsrepresenting a reference line in which the ADV is to follow; selecting asecond set of reference points along the reference line corresponding tothe first set of reference points, including iteratively performing,selecting a current reference point from the first set of referencepoints; determining a sampling distance along the first set of referencepoints based on the currently selected reference point using a nonlinearalgorithm; and selecting a next reference point along the first set ofreference points based on the determined sampling distance such that adensity of the selected reference points closer to the ADV is higherthan a density of the selected reference points farther away from theADV; and planning a trajectory for the ADV using the second set ofreference points to control the ADV.
 16. The system of claim 15, whereindetermining a sampling distance further comprises: determining a minimumsampling distance; and determining a maximum sampling distance, whereinthe sampling distance is determined based on the minimum samplingdistance and the maximum sampling distance.
 17. The system of claim 16,wherein the sampling distance is determined further in view of a lengthof the reference line.
 18. The system of claim 15, wherein the secondset of reference points includes points that are separated by a distanceapproximately equal to the minimum sampling distances near the ADV and adistance approximately equal to the maximum sampling distances away fromthe ADV.
 19. The system of claim 15, wherein the second set of referencepoints has a density that gradually decreases away from the ADV.
 20. Thesystem of claim 15, wherein the nonlinear algorithm is an inverse of anexponential function.
 21. The system of claim 15, wherein the nonlinearalgorithm is ${{f(s)} = \frac{A}{1 + e^{{Bs} + C}}},$ wherein s is adistance away from the ADV, A is a maximum sampling distance, C is alogarithmic of A divided by a minimum sampling distance minus one, and Bis a logarithmic of A/((A−σ)−1), minus C, divided by a length of thereference line, wherein σ is a fractional number.